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Register a new userStellar archaeology with Gaia: the Galactic white dwarf population
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Gaia will identify several 1e5 white dwarfs, most of which will be in the solar neighborhood at distances of a few hundred parsecs. Ground-based optical follow-up spectroscopy of this sample of stellar remnants is essential to unlock the enormous scientific potential it holds for our understanding of stellar evolution, and the Galactic formation history of both stars and planets.
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We present ten new ultra-cool dwarfs in seven wide binary systems discovered using $textit{Gaia}$ DR2 data, identified as part of our $textit{Gaia}$ Ultra-Cool Dwarf Sample project. The seven systems presented here include an L1 companion to the G5 IV star HD 164507, an L1: companion to the V478 Lyr AB system, an L2 companion to the metal-poor K5 V star CD-28 8692, an M9 V companion to the young variable K0 V star LT UMa, and three low-mass binaries consisting of late Ms and early Ls. The HD 164507, CD-28 8692, V478 Lyr, and LT UMa systems are particularly important benchmarks, because the primaries are well characterised and offer excellent constraints on the atmospheric parameters and ages of the companions. We find that the M8 V star 2MASS J23253550+4608163 is $sim$2.5 mag overluminous compared to M dwarfs of similar spectral type, but at the same time it does not exhibit obvious peculiarities in its near-infrared spectrum. Its overluminosity cannot be explained by unresolved binarity alone. Finally, we present an L1+L2 system with a projected physical separation of 959 au, making this the widest L+L binary currently known.
A tutorial for the Stellar Abundances for Galactic Archaeology (SAGA) database is presented. This paper describes the outline of the database, reports the current status of the data compilation and known problems, and presents plans for future updates and extensions.
We use 156 044 white dwarf candidates with $geq5sigma$ significant parallax measurements from the Gaia mission to measure the velocity dispersion of the Galactic disc; $(sigma_U,sigma_V,sigma_W) = (30.8, 23.9, 20.0)$ km s$^{-1}$. We identify 142 objects that are inconsistent with disc membership at the $>5sigma$ level. This is the largest sample of field halo white dwarfs identified to date. We perform a detailed model atmosphere analysis using optical and near-infrared photometry and parallaxes to constrain the mass and cooling age of each white dwarf. The white dwarf cooling ages of our targets range from 7 Myr for J1657+2056 to 10.3 Gyr for J1049-7400. The latter provides a firm lower limit of 10.3 Gyr for the age of the inner halo based on the well-understood physics of white dwarfs. Including the pre-white dwarf evolutionary lifetimes, and limiting our sample to the recently formed white dwarfs with cooling ages of $<500$ Myr, we estimate an age of $10.9 pm 0.4$ Gyr (internal errors only) for the Galactic inner halo. The coolest white dwarfs in our sample also give similar results. For example, J1049-7400 has a total age of 10.9-11.1 Gyr. Our age measurements are consistent with other measurements of the age of the inner halo, including the white dwarf based measurements of the globular clusters M4, NGC 6397, and 47 Tuc.
M dwarf stars are the most common stars in the Galaxy, dominating the population of the Galaxy by numbers at faint magnitudes. Precise and accurate stellar parameters for M dwarfs are of crucial importance for many studies. However, the atmospheric parameters of M dwarf stars are difficult to be determined. In this paper, we present a catalog of the spectroscopic stellar parameters ($T_{eff}$ and [M/H]) of $sim$ 300,000 M dwarf stars observed by both LAMOST and Gaia using Stellar Label Machine (SLAM). We train a SLAM model using LAMOST spectra with APOGEE Data Release 16 (DR16) labels with $2800 lt T_{eff} lt 4500$K and $-2 lt [M/H] lt 0.5$ dex. The SLAM $T_{eff}$ is in agreement to within $sim 50$K compared to the previous study determined by APOGEE observation, and SLAM [M/H] agree within 0.12 dex compared to the APOGEE observation. We also set up a SLAM model trained by BT-Settl atmospheric model, with random uncertainties (in cross-validation) to 60K and agree within $sim 90$K compared to previous study.
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